Hierarchical Component Analysis Research Articles

Modeling innovative work behavior through leadership behaviors in the hospitality industry; the role of work–family enrichment

PurposeThis study aims to examine authentic leadership behavior (ALB) and empowering leadership behavior (ELB) as predictors of innovative work behavior (IWB) while assessing the mediating role of work-family enrichment (WFE).Design/methodology/approachThrough the explanatory research design, data was retrieved from 303 respondents from 16 small-sized hotels in Ghana and analyzed quantitatively using descriptive and inferential statistics (hierarchical component analysis within PLS-SEM).FindingsThe study’s findings revealed that both ALB and ELB positively and significantly relate to IWB. Additionally, our study found that WFE partially mediated the nexus between ALB and IWB as well as ELB and IWB.Practical implicationsThe findings of our study imply that in the pursuit of excellence, ALB and ELB in the hospitality industry are crucial, as they not only nurture employee potential but also help employees to thrive both at home and at the workplace.Originality/valueThe hospitality literature lacks a comprehensive understanding of the underlying mechanism by which ALB and ELB influence IWB. To the best of the authors' knowledge, our study is the first empirical study conducted on ALB, ELB, WFE, and IWB in the hospitality industry.

How do physicochemical properties contribute to inhibitory activity of promising peptides against Zika Virus NS3 protease?

Flavivirus diseases' cycles, especially Dengue and Yellow Fever, can be observed all over Brazilian territory, representing a great health concern. Additionally, there are no drugs available in therapy. In this scenario, in silico methodologies were applied to obtain physicochemical properties, as well as to better understand the ligand-biological target interaction mode of 20 previously reported NS2B/NS3 protease inhibitors of Dengue virus. Since catalytic site of flavivirus hold similarities, such as the same catalytic triad (His51, Asp75 e Ser135), the ability of this series of molecules to fit in Zika NS3 domains can be achieved. We performed an exploratory data analysis, using statistical methodologies, such as PCA (Principal Component Analysis) and HCA (Hierarchical Component Analysis), to assist the comprehension of how physicochemical properties impact the interaction observed by the docking studies, as well as to build a correlation between the respective ranked characteristics. Based on these previous studies, peptides were selected for the dynamics simulations, which were useful to better understand the ligand-protein interactions. Information relating to, for instance, energy, ΔG, average number of hydrogen bonds and distance from Ser135 (one of the main amino acids in the catalytic pocket) were discussed. In this sense, peptides 15 (considering ΔG value and Hbond number), 7 (ΔG and energy) and 1, 6, 7 and 15 (the proximity to Ser135 throughout the dynamics simulation) were highlighted as promising. Those interesting results could contribute to future studies regarding Zika virus drug design, since this infection represents a great concern in neglected populations. The models were constructed in the ChemDraw software. The ligand parametrization was performed in the CHEM3D 17.0, UCSF Chimera. Docking simulations were carried out in the GOLD software, after the redocking validation. We used ASP as the function score. Additionally, for dynamics simulations we applied GROMACS software, exploring, mainly, free binding energy calculations. Exploratory analysis was carried out in Minitab 17.3.1 statistical software. Prior to the exploratory analysis, data of quantum chemical properties of the peptides were collected in Microsoft Excel spreadsheet and organized to obtain Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA).

Monitoring a metabolic profile of wheat by using FTIR spectroscopy and chemometric methods — concept studies

Changes in plants under the influence of a variety of chemical and physical factors are reflected in metabolomic changes. To date, there are very few methods that would allow studying metabolic changes occurring in single cells. Spectroscopic methods especially combined with the chemometrics methods are a very good tool to investigate such changes in metabolomics. Tracking changes in plants is of particular importance in industry, especially when studying how the use of fertilizers affects plants. In this paper, we present preliminary research asa concept of proof to examine whether the use of FTIR (Fourier transform infrared spectroscopy) helps to monitor the changes in the metabolomic profile of the plants. For preliminary research, four species of cereals and cuckooflower were used. In this step, it was possible to verify the differences in metabolites that are produced by plants belonging to different families. Then one species of grain was selected and subjected to eleven different physical and chemical factors. Next, the research was expanded to determine the optimal concentration of hydrogen peroxide. FTIR spectra of leaves and extracts of the plants were obtained for all experimental groups and then analyzed with the use of chemometric methods: HCA (hierarchical component analysis) and PCA (Principal component analysis). Those methods were used to help in the interpretation of metabolic changes resulting in the plant in response to external factors.

Establishing a simple selection method of high hypericin-producing adventitious root lines from leaf explants of Hypericum perforatum var. Topaz

ABSTRACT Hundreds of adventitious root lines from Hypericum perforatum L. were examined for their hypericin content in order to establish a system for studying the production of hypericin in adventitious root lines. The effects of the medium consistency (solid and liquid) and the effects of three different auxins (Indole-3-butyric acid (IBA), Indole-3-acetic acid (IAA), and 1-Naphthaleneacetic acid (NAA)) on the induction of adventitious roots in leaf explants were studied. In a half-strength MS liquid medium with 2 mg/L IBA concentration, adventitious roots developed from H. perforatum leaves, whereas NAA and IAA caused callus-like and low branch roots. HPLC-UV was used to profile hypericin content variations in 100 H. perforatum adventitious root lines. Hierarchical component analysis (HCA) was used for selecting elite lines. After cluster analysis and selecting high-performance lines in terms of hypericin, five elite lines were chosen and the amount of hypericin production, as well as the phenol and flavonoid content, was analysed.

Pollen morphology of family Thymelaeaceae Juss. in India and its taxonomic implications

We provide pollen morphology and ultrastructure of 26 plant taxa, comprising 14 genera, 21 species, 1 sub-species and 3 varieties, of the Thymelaeaceae family from the Indian sub-continent using light micsoscopic (LM) and scanning electron microscopic (SEM) techniques. The principal aim was to provide new and useful information regarding the extent of the pollen morphological diversity among the Thymelaeaceae taxa and, thus, contribute to a better understanding of the taxonomy of the studied taxa. The pollen grains of the studied taxa are essentially radially symmetrical, apolar, (released as) monad, pantoporate with a typical crotonoid pattern, and spheroidal (mostly) in shape; but widespread variations with respect to the number and dispensation of pores, size, exine (ectoexine and endoexine) thickness, and the shape itself (spheroidal, sub-spheroidal and prolate spheroidal), as well as in the amb (outline) have been also recorded. Pollen identification keys, based on the pollen micro-morphological attributes, were also constructed in order to easily identify the 26 studied Thymelaeceae taxa up to species and/or varieties level (high taxonomic resolution, showing taxonomic significance). The pollen morphological attributes of the 26 studied plant taxa of the Thymelaeaceae family were subjected to Principal Component Analysis (PCA) and Hierarchical Component Analysis (HCA), which suggest four clusters. The cophenetic correlation coefficient also validates the four group of clustering. The study will significantly help in the precise pollen identification in fossil (pollen) records for the reconstruction of vegetation dynamics and contemporary climate change during the Quaternary Period in India. Moreover, the present study also helps in determining the phylogenetic and evolutionary trend on the basis of the presence or absence of exine ornamentation and having more or less apertures among the pollen grains of the 26 studied Thymelaeaceae taxa.

Network-based diagnostic probability estimation from resting-state functional magnetic resonance imaging.

Brain functional connectivity is a useful biomarker for diagnosing brain disorders. Connectivity is measured using resting-state functional magnetic resonance imaging (rs-fMRI). Previous studies have used a sequential application of the graphical model for network estimation and machine learning to construct predictive formulas for determining outcomes (e.g., disease or health) from the estimated network. However, the resulting network had limited utility for diagnosis because it was estimated independent of the outcome. In this study, we proposed a regression method with scores from rs-fMRI based on supervised sparse hierarchical components analysis (SSHCA). SSHCA has a hierarchical structure that consists of a network model (block scores at the individual level) and a scoring model (super scores at the population level). A regression model, such as the multiple logistic regression model with super scores as the predictor, was used to estimate diagnostic probabilities. An advantage of the proposed method was that the outcome-related (supervised) network connections and multiple scores corresponding to the sub-network estimation were helpful for interpreting the results. Our results in the simulation study and application to real data show that it is possible to predict diseases with high accuracy using the constructed model.

Big Data Analytics Capability and Business Alignment for Organizational Agility

This paper investigates the impact of big data analytics capability (BDAC) on organizational agility under the moderating effect of BDAC–business alignment and its impact on performance through organizational agility. Data from a matched-pair survey of business, data technology, and financial executives in 161 organizations were used to examine the proposed research model. This paper used partial least squares–structural equation modeling and hierarchical component analysis to examine the data. The results suggest a positive mediation role of organizational agility in the relationship between big data analytics capability and organizational performance, except that the mediation effect of operational adjustment agility on BDAC and market performance is not statistically significant. This study also finds that alignment between the business strategy and the big data analytics strategy enhances the relationship between BDAC and market responsiveness agility. It proposes a new perspective which is to realize the value of BDAC in enhancing agility and performance.

Correlation between endometriomas volume and Raman spectra. Attempting to use Raman spectroscopy in the diagnosis of endometrioma

The formation of the uterus lining, i.e. the endometrium, outside the uterus (ex. in the abdominal cavity,ovaries,or anywhere in the body) is called endometriosis. The presence of endometrial tissue present in the ovaries, thickens after menstruation, leading to menstrual-like bleeding and to the formation of chocolate cyst (Endometrioma) because of the accumulation of old, brown blood in the ovary. It is still unknown, what triggers the development ofendometrioma. However,it leads to excessive bleeding during menstrual periods or abnormal bleeding between periods and infertility. Endometriosis is often first diagnosed in those who seek medical attention for infertility. Therefore, new markers of endometrioma as well as new methods of its diagnosis are sought. In this study we used Raman spectra of serum collected from 50 healthy women and 50 women suffering from endometriosis. The obtained Raman data were used in multivariateanalysis to determine the Raman range, which can be used for endometriomadiagnostics. Partial Least Square (PLS), Principal Component Analysis (PCA) and Hierarchical Component Analysis (HCA) showed, that it is possible to distinguish between the serum collected from healthy and un-healthy women using the Raman range between 800 cm−1 and 1800 cm−1 and between 2956 cm−1 and 2840 cm−1, while the first range corresponds to the fingerprint region and the second one to lipids vibrations. Consequently, the Pearson correlation test showeda significantpositive correlation betweenvaluesoflipidintensity in Raman spectra and volume of endometriomas. Summarizing, Raman spectroscopy can be a helpful tool in endometrioma diagnosis and the lipid vibrations are candidates for being a spectroscopic marker of the disease being studied.

Detection of the chemical changes in blood, liver, and brain caused by electromagnetic field exposure using Raman spectroscopy, biochemical assays combined with multivariate analyses

The effects of the electromagnetic field on living organisms have been studied for several years. In this article, we showed what kind of cold change an extremely low-frequency electromagnetic field (ELF-MF) exposure 500 μT 50 Hz by using a Meritt Coil System causes in the samples of the brain and liver samples. To measure oxidative load, we measured malondialdehyde (MDA) and glutathione (GSH) levels. To identify the chemical changes, we collected Raman spectra of cerebellum, left brain, right brain and liver tissue from the control group of animals and from the animal, which were exposed to an electromagnetic field (ELF-MF group). Obtained results showed, that lipid peroxidation was increased and the antioxidant response was decreased. In the brain samples the shift of peaks corresponding to the amide III vibrations existed after ELF-MF exposure. Structural changes were detected in CH2 vibrations originating from lipids in both hemispheres. Additionally, the number of amide III bonds was increased with ELF-MF exposure in the cerebellum and left-brain tissue. In liver tissue higher Raman intensities were visible in the tissues from the ELF-MF group. In this group electromagnetic field also caused structural changes in lipids. Principal component analysis (PCA) showed, that it is possible to distinguish ELF-MF and control groups. Consequently, hierarchical component analysis (HCA) showed that tissues from ELF-MF and control groups separately created similarity with the groups. Obtained results suggest that the electromagnetic field caused structural and quantitative chemical changes in brain and liver tissue. Additionally, present data suggest that ELF-MF plays an important role in the regulation of enzyme activity and has effects on biochemical processes, possibly improved by production of ROS.

Differential of cholangiocarcinoma disease using Raman spectroscopy combined with multivariate analysis

Cholangiocarcinoma (CCA) is a type of cancer, which 5-year survival is lower than 20 %, and which is detected mostly in advanced stage of the disease. Unfortunately, there are no diagnostic tools, which could show changes in the body indicating the development of the disease. Therefore, in this study, we investigate Raman spectroscopy as a promising analytical tool in medical diagnostics and as a method, which would allow to distinguish between healthy patients and patients suffering from cholangiocarcinoma. The obtained Raman spectra showed, that lower intensities of peaks corresponding to amino acids and proteins, as well as higher intensities of peaks originating from lipids vibrations were observed in healthy individuals in comparison with cancer patients. Moreover, Partial Last Square (PLS), Principal Component Analysis (PCA) and Hierarchical Component Analysis (HCA) of Raman spectra indicate that the ranges between 800 cm−1 and 1800 cm−1, 3477 cm−1 –3322 cm−1 and 1394 cm−1 –1297 cm−1 allow to distinguish cancer patients from healthy ones. The obtained results showed, that Raman spectroscopy is a good candidate, to become in future one of the diagnostic tools of Cholangiocarcinoma.

Paper-based colorimetric sensor array for the rapid and on-site discrimination of green tea samples based on the flavonoid composition.

Green tea is a worldwide appreciated food product with Chinese production estimated to reach over 3m tons in 2027 and with many valuable health effects. The development of analytical methods to discriminate among green tea samples is induced by economic benefits and to avoid deliberate origin mislabeling and adulteration. In this study, we present a paper-based colorimetric sensor array comprised of six ordinary reagents tailored for the discrimination of green tea extracts of different brands according to differences in the composition of flavonoids. The colorimetric array was rationally designed based on indicators that differentially react with a variety of flavonoids via specific functional groups. 4 μL of each reagent was impregnated onto the paper surface followed by the addition of the green tea extract. After 1 minute, digital images were acquired using a smartphone and the color changes were employed to build differential maps with a unique fingerprint for each green tea sample. Moreover, principal component analysis (PCA) and hierarchical component analysis (HCA) were employed to successfully discriminate among the samples, enabling the origin and adulteration identification of the samples. Therefore, this study provides a simple, effective, low-cost, and portable method for quick discrimination and quality control of green tea samples.

Effect of Smoking and Its Cessation on the Transcript Profile of Peripheral Monocytes in COPD Patients.

RationaleSmoking is the primary cause of chronic obstructive pulmonary disease (COPD); however, only 10–20% of smokers develop the disease suggesting possible genomic association in the causation of the disease. In the present study, we aimed to explore the whole genome transcriptomics of blood monocytes from COPD smokers (COPD-S), COPD Ex-smokers (COPD-ExS), Control smokers (CS), and Control Never-smokers (CNS) to understand the differential effects of smoking, COPD and that of smoking cessation.MethodsExploratory analyses in form of principal component analysis (PCA) and hierarchical component analysis (uHCA) were performed to evaluate the similarity in gene expression patterns, while differential expression analyses of different supervised groups of smokers and never smokers were performed to study the differential effect of smoking, COPD and smoking cessation. Differentially expressed genes among groups were subjected to post-hoc enrichment analysis. Candidate genes were subjected to external validation by quantitative RT-PCR experiments.ResultsCNS made a cluster completely segregated from the other three subgroups (CS, COPDS and COPD-ExS). About 550, 8 and 5 genes showed differential expression, respectively, between CNS and CS, between CS and COPD-S, and between COPD-S and COPD-ExS. Apoptosis, immune response, cell adhesion, and inflammation were the top process networks identified in enrichment analysis. Two candidate genes (CASP9 and TNFRSF1A) found to be integral to several pathways in enrichment analysis were validated in an external validation experiment.ConclusionControl never smokers had formed a cluster distinctively separated from all smokers (COPDS, COPD-ExS, and CS), while amongst all smokers, control smokers had aggregated in a separate cluster. Smoking cessation appeared beneficial if started at an early stage as many genes altered due to smoking started reverting towards the baseline, whereas only a few COPD-related genes showed reversal after smoking cessation.

Unique physicochemical properties and rare reducing sugar trehalulose mandate new international regulation for stingless bee honey

Stingless bee honey (SBH) of four stingless bee species (Heterotrigona itama, Geniotrigona thoracica, Tetragonula carbonaria, and Tetragonula hockingsi) from two geographic regions (Malaysia and Australia, n = 36) were studied for their physicochemical parameters, including total phenolic and multi-elemental contents. Sugar analysis confirmed the prominent presence of trehalulose in all samples. All SBH failed to meet the CODEX Standard for honey moisture, free acidity, and total fructose plus glucose levels. One-way ANOVA, principal component analysis (PCA) and hierarchical component analysis (HCA) confirm distinctive differences between Australian and Malaysian SBH with Australian SBH having significantly (P < 0.05) higher pH, total phenolic, total mineral, and electrical conductivity attributes. Toxic elements in all SBH were much lower than standard CODEX limit. The species G. thoracica produced honey with significantly (P < 0.05) higher trehalulose than both Australian bee species. This research will add value to SBH and hasten new international standards ensuring commercial viability, safety, quality, and marketability of SBH.

Characterization of virgin olive oils obtained from minor Tunisian varieties for their valorization

Several compositional parameters (fatty acids, tocopherols, polar phenols and volatiles) in virgin olive oils from varieties located at the center of Tunisia, namely Baldi, Chemchali, Neb Jmel, Tounsi and Besbessi from Gafsa and Chemchali and Sehli cultivated in Sidi Bouzid, were studied. To the best of our knowledge, this is the first study focusing on the chemical composition of virgin olive oils (VOO) from Sidi Bouzid. Significant differences were found between varieties from Sidi Bouzid and Gafsa in monounsaturated saturated fatty acid levels, some volatile compounds and γ-tocopherol amounts. Notably, Tounsi and Baldi VOOs showed the highest six carbon atoms alcohol contents, whereas Neb Jmel VOO revealed the highest aldehyde six carbon atoms content. Fatty acid composition, as well as tocopherol content, showed differences among different VOO. Principal component analysis and hierarchical component analysis on polar phenols, fatty acids, tocopherols and volatiles data evidenced a good discrimination among oils from the different investigated varieties.

Chemical QR Code: A simple and disposable paper-based optoelectronic nose for the identification of olive oil odor

Olive oil is an appreciated food product with high nutritional value, besides being an essential component in many culture diets. In this study, we present for the first time the application of a simple and non-invasive paper-based optoelectronic nose designed in a QR code configuration to evaluate the odor of olive oils. The chemical QR code was fabricated by the addition of 12 dyes, which provided high dimensional data resulting from the interaction between the volatile compounds and the colorimetric array. The color changes were employed to build differential maps with a unique fingerprint (i) to discriminate between olive oil and other edible oil samples; (ii) to quantify nonanaldehyde as an oxidation marker; and (iii) to identify oxidized oils through principal component analysis (PCA) and hierarchical component analysis (HCA). By developing suitable mobile apps, we anticipate the employment of the chemical QR code for portable, low-cost, and in-situ evaluation of food product quality.

Identification of therapeutic targets from genetic association studies using hierarchical component analysis

BackgroundMapping disease-associated genetic variants to complex disease pathophysiology is a major challenge in translating findings from genome-wide association studies into novel therapeutic opportunities. The difficulty lies in our limited understanding of how phenotypic traits arise from non-coding genetic variants in highly organized biological systems with heterogeneous gene expression across cells and tissues.ResultsWe present a novel strategy, called GWAS component analysis, for transferring disease associations from single-nucleotide polymorphisms to co-expression modules by stacking models trained using reference genome and tissue-specific gene expression data. Application of this method to genome-wide association studies of blood cell counts confirmed that it could detect gene sets enriched in expected cell types. In addition, coupling of our method with Bayesian networks enables GWAS components to be used to discover drug targets.ConclusionsWe tested genome-wide associations of four disease phenotypes, including age-related macular degeneration, Crohn’s disease, ulcerative colitis and rheumatoid arthritis, and demonstrated the proposed method could select more functional genes than S-PrediXcan, the previous single-step model for predicting gene-level associations from SNP-level associations.

Evaluation of pollution status of groundwater resources of parts of Owerri metropolis and environs, Southeastern Nigeria, using health risk and contamination models

Some groundwater resources within Owerri Metropolis and environs, Southeastern Nigeria were evaluated to ascertain the level of groundwater contamination as well as the intrinsic health risk that may result from the oral ingestion of the groundwater from the study area. Groundwater samples from 27 boreholes were taken and analysed using standard procedures. The results revealed pH levels (6.04 ± 0.09 to 6.47 ± 0.06) which were lower than the World Health Organisation (WHO) permissible limit. Elevated levels of lead (0.01 ± 0.01 to 0.08 ± 0.02 mg/L) concentration above the WHO standard for safe drinking water were observed in some samples. Though the pollution load index for the area was low, however, the contamination factor revealed lead (Pb) contamination of the samples with positive correlations existing between the metallic pollutants. Electrical conductivity and dissolved solids showed a very strong correlation with r = 1. However, the result of the water quality index revealed that the groundwater samples were within good to excellent water quality. The principal component analysis of the samples grouped the metallic pollutants into three different components, suggesting similarity in the source of occurrence with only one coherent cluster established by the hierarchical component analysis indicating their association in terms of occurrence. The individual hazard quotient and hazard index of the metallic pollutants for adults and children were < 1, which implies slight or no negative health hazard from the intake of the water samples, though the presence of Pb in some samples may pose some health risk. Similarly, low chronic daily intake values were recorded. Though low PLI and CDI were recorded across the area in addition to HQ and HI values < 1, appropriate treatment is recommended for the studied groundwater samples before consumption to prevent an impending health challenge due to possible bioaccumulation of metallic contaminants which may take place over time.

English

The present work consists of the evaluation of antimicrobial activity and inhibition of the enzyme acetylcholinesterase (AChE) of fixed oils and hexane extracts of nine fruits with the following native names: abiu (Pouteria caimito), acerola (Malpighia emarginata), araca (Psidium cattleianum), bacupari (Rheedia gardneriana), biriba (Rollinia mucosa), camu-camu (Myrciaria dubia), fruta-do-conde (Annona squamosa), graviola (Annona muricata) and tapereba (Spondias mombin L.). Different evaluations were carried out with different parts of the fruits, pulp, seed and barks. The antimicrobial assay was carried out with the following microorganisms: Candida albicans ATCC 18804, Staphylococcus aureus ATCC 29212, Bacillus cereus ATCC 11778, Escherichia coli ATCC 25922 and Salmonella typhimurium ATCC 14028. Of these microorganisms, the best inhibition results were obtained for yeast C. albicans with percent inhibition of 94.46% by tapereba barks extracts, acerola barks (87.12%), araca seed (85.23%) and tapereba pulp (85.22%). Against the bacteria tested, percent inhibition was low, showing that the extracts have good antifungal selectivity. Some extracts were able to inhibit the enzyme AChE and high percentage of inhibition was observed for the oils, especially from biriba barks, with 86.39% inhibition, tapereba seeds with 62.17% and acerola pulp with 52.18%. Methods of Multivariate Analysis were applied through Principal Component Analysis (PCA) and Hierarchical component analysis (HCA), to establish correlations and groupings between the data obtained, justifying 82.3% of cases for pulps, 73.2% for the barks and 65.7% for the seeds according to the PCA. Key words: Bacteria, yeasts, Alzheimer, principal component analysis (PCA), hierarchical component analysis (HCA).

MULTIVARIATE STATISTICAL ANALYSIS FOR THE ASSESSMENT OF GROUNDWATER QUALITY IN SEMARANG LOWLAND AREA

Groundwater is the primary water resources for the human activities in Semarang lowland area. Serious threats against sustainable development concerning groundwater are inevitable. Therefore, it is essential to assess groundwater quality. The primary objective of the research is assessing groundwater quality using the multivariate statistical analysis using Principal Component Analysis (PCA) and Hierarchical Component Analysis (HCA) to evaluate 30 groundwater samples which are collected from dug wells. The results show the concentration of pH, Fe3+, Cl-, Mn2+, SO42-, NO2-, exceeding the Indonesian water drinking standard. Cluster and factorial analysis showed three primary factors in groundwater. Factor 1 explained that 63.5% from total variant with EC, CaCO3, Mg2+, K+, Na+, Cl- and TDS show a potential contamination source from seawater intrusion. Factor 2 explained that 13.5% from total variant with pH and NO3-show a potential contamination source from household waste. Factor 3 explained that 10.4% from total variant with Fe3+ is assumed to be contaminants from rocks around Semarang.

Wild edible mushrooms from Mediterranean region: Metal concentrations and health risk assessment.

Worldwide, among the forest products, wild edible mushrooms constitute an important part because they represent food source as well as income source for many local communities. Thirteen essential elements (Ca, Co, Cr, Cu, Fe, K, Mg, Mn and Zn) and non-essential elements (Al, Cd, Ni and Pb) in wild edible mushrooms from six families (Agaricaceae, Auriculariaceae, Hygrophoraceae, Russulaceae, Suillaceae, and Tricholomataceae) originated from the Mediterranean region of Turkey were determined. Major element was K, followed by Ca, Mg, Fe and Al. Concentrations of detected elements were in the range from 0.05mg/kg (Co) to 141,400mg/kg (K). Health Risk Index for elements that may pose health problems indicated that safe limits were exceeded for Cd (L. nuda, L. decastes, M. exscissa, R. albonigra, R. delica and T. terreum), Ni (A. auricula-judae and S. luteus) as well as for Fe (A. auricula-judae and M. paedida). In Arpacık village and Deliosman village areas the highest number of wild edible mushrooms with HRI>1 was collected. The differences and similarities between mushroom species were established by Principal Component Analysis and Hierarchical Component Analysis.